AFC Women's Champions League Preliminary Round Group C stats & predictions

Cada año trae consigo nuevas estrellas emergentes que capturan la atención tanto de aficionados como de expertos deportivos
ránulo Estos son algunos talentos jóvenes destacados en el Grupo C
ránulo

• 
  ránuloJugadora Alpha (Equipo A):
  ránulo Conocida por su velocidad impresionante
  ránulo Ha sido fundamental
  ránulo En varias victorias clave
  ránulo
• 
  ránuloJugadora Beta (Equipo B):
  ránulo Destaca por su habilidad
  ránulo En tiros libres
  ránulo Ha marcado
  ránulo Al menos dos goles decisivos
  ránulo
• 
  ránuloJugadora Gamma (Equipo C):
  ránulo Su visión
  ránulo Y pases precisos
  ránulo La convierten
  ránulo En una pieza clave
  ránulo Del medio campo
  ránulo
• 
  ránuloJugadora Delta (Equipo D):
  ránulo Reconocida
  ránulo Por su capacidad
  ránulo Para realizar jugadas espectaculares

  Cuídate siempre al seguir este tipo de deportes intensos.Influencia Cultural del Fútbol Femenino en Asia

  Más allá del aspecto deportivo,

  Empoderamiento Femenino: <|repo_name|>mattsparks/caffe2<|file_sep|>/caffe2/python/brew.py from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import collections import logging import numpy as np from caffe2.proto import caffe2_pb2 class Brew(object): """A brew object represents an operator graph. The operator graph can be created by calling `brew.mixed` or `brew.sgd`. The created graph can then be optimized by calling `brew.step` and executed using the resulting `net`. The brew object is mutable and the resulting graph can be modified by adding, removing or changing operators. It is also possible to execute the graph using the `run` method. This method executes the graph and returns the resulting blobs. Example: brew = brew.mixed() brew.net.GivenTensorFill("X", [1], "ConstantFill", value=1) brew.net.SquaredL2Distance(["X", "Y"], "Z") brew.step() Z = brew.run(X=np.ones([1]), Y=np.zeros([1])) assert np.allclose(Z[0], np.ones([1])) """ class Net(object): """A Net object represents an operator graph. The Net object contains one or more operators. Each operator is identified by its index in the net and has two attributes: - `operator`: the proto definition of the operator. - `input_blobs`: the list of input blobs for the operator. - `output_blobs`: the list of output blobs for the operator. Example: brew = brew.mixed() op = brew.net.GivenTensorFill("X", [1], "ConstantFill", value=1) assert op.operator.type == "ConstantFill" assert op.input_blobs == [] assert op.output_blobs == ["X"] """ def __init__(self): self._operators = [] @property def operators(self): return self._operators @property def blob_names(self): blob_names = set() for op in self._operators: blob_names.update(op.input_blobs) blob_names.update(op.output_blobs) return list(blob_names) @property def blob_types(self): blob_types = {} for op in self._operators: for b in op.input_blobs: if b not in blob_types: blob_types[b] = None for b in op.output_blobs: if b not in blob_types: blob_types[b] = None else: # Overwrite with None so that we know it's an output. blob_types[b] = None return blob_types def _new_operator(self): index = len(self._operators) op_proto = caffe2_pb2.OperatorDef() op_proto.index = index return Operator(op_proto) def _add_operator(self, op): self._operators.append(op) def _remove_operator(self, index): self._operators.pop(index) def _replace_operator(self, index, new_op): self._operators[index] = new_op def clear(self): """Clears all operators.""" self._operators.clear() # TODO(mihaimaruseac): Replace with more specific methods. def GivenTensorFill(self, output, shape, type="GivenTensorFill", **kwargs): """Creates an operator that fills the given output tensor with given values. Args: output: string representing the name of the output tensor. shape: list of integers representing the shape of the output tensor. type: string representing the type of the operator (optional). Defaults to "GivenTensorFill". **kwargs: additional arguments for defining the operator. Returns: An Operator object that represents this operator. Example: brew = brew.mixed() op = brew.net.GivenTensorFill("X", [1], "ConstantFill", value=1) assert op.operator.type == "ConstantFill" assert op.input_blobs == [] assert op.output_blobs == ["X"] """ return self._create_and_add(type=type, outputs=[output], shape=shape, **kwargs) def GivenTensorIntializer(self, output, initializer, type="GivenTensorIntializer", **kwargs): """Creates an operator that fills the given output tensor with values generated from an initializer. Args: output: string representing the name of the output tensor. initializer: Initializer object used to generate values. type: string representing the type of the operator (optional). Defaults to "GivenTensorIntializer". **kwargs: additional arguments for defining the operator. Returns: An Operator object that represents this operator. Example: brew = brew.mixed() init = caffe2_pb2.XavierWeightInitializer() init.filler_type = "gaussian" init.std = .01 init.mean = .001 init.shape.extend([10]) init.shape.extend([10]) op = brew.net.GivenTensorIntializer("X", init)